02 - Tables

Author

Yann Say

Published

January 2, 2025

Outputs - Wide tables

library(presentresults)

my_results_table <- presentresults::presentresults_resultstable

The framework is built around 4 steps: cleaning, composition, analysis, outputs

  • Cleaning: Any manipulation to go from the raw data to the clean data
  • Composition: Any manipulation before the analysis e.g. adding indicators, adding information from loop, main dataset, or any other dataset (e.g. previous round), aok aggregation, etc.
  • Analysis: Any manipulation regarding only the analysis.
  • Outputs: Any manipulation to format the outputs. Outputs are created from the results table, from the stat + analysis key

The following section will present some introduction about the outputs.

There are currently two types of table:

  • one that have the variables in the rows and the disagregation in the columns,
  • one that have the disagregation in the rows and the variables in the columns.

There are two steps to turn a results table:

  • Turn the long results table to a large results table.
  • Format and export it to Excel.
create_*

create_* functions will create, transform something, e.g. creating a cleaning log with the checks to be filled, create analysis results table, create an output.

Outputs from create_* functions outputs can be in different shape, format, etc.

create_* function is catch-all.

create_*_group_x_variable

Wide table with the groups in the rows and the variables in the columns.

my_results_table %>% 
  create_table_group_x_variable() %>% 
  create_xlsx_group_x_variable(file_path = "../outputs/04 - example - group_x_variable.xlsx", overwrite = T)

create_*_variable_x_group

Wide table with the variables in the rows and the groups in the columns.

my_results_table %>% 
  create_table_variable_x_group() %>%
  create_xlsx_variable_x_group(file_path = "../outputs/05 - example - variable_x_group.xlsx", overwrite = T)

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